Person:

Dupont, Pierre

Magnetic resonance navigation (MRN) offers the potential for real-time steering of drug particles and cells to targets throughout the body. In this technique, the magnetic gradients of an MRI scanner perform image-based steering of magnetically-labelled therapeutics through the vasculature and into tumours. A major challenge of current techniques for MRN is that they alternate between pulse sequences for particle imaging and propulsion. Since no propulsion occurs while imaging the particles, this results in a significant reduction in imaging frequency and propulsive force. We report a new approach in which an imaging sequence is designed to simultaneously image and propel particles. This sequence provides a tradeoff between maximum propulsive force and imaging frequency. In our reported example, the sequence can image at 27 Hz while still generating 95% of the force produced by a purely propulsive pulse sequence. We implemented our pulse sequence on a standard clinical scanner using millimetre-scale particles and demonstrated high-speed (74 mm/s) navigation of a multi-branched vascular network phantom. Our study suggests that the magnetic gradient magnitudes previously demonstrated to be sufficient for pure propulsion of micron-scale therapeutics in magnetic resonance targeting (MRT) could also be sufficient for real-time steering of these particles.

A novel approach to constructing robots is based on concentrically combining pre-curved elastic tubes. By rotating and extending the tubes with respect to each other, their curvatures interact elastically to position and orient the robot's tip, as well as to control the robot's shape along its length. Since these robots form slender curves, they are well suited for minimally invasive medical procedures. A substantial challenge to their practical use is the real-time solution of their kinematics that are described by differential equations with split boundary equations. This paper proposes a numerically efficient approach to real-time position control. It is shown that the forward kinematics are smooth functions that can be pre-computed and accurately approximated using Fourier series. The inverse kinematics can be solved in real time using root finding applied to the functional approximation. Experimental demonstration of real-time position control using this approach is also described.

Properly selected port sites for robot-assisted coronary artery bypass graft (CABG) improve the efficiency and quality of these procedures. In clinical practice, surgeons select port locations using external anatomic landmarks to estimate a patient’s internal anatomy. This paper proposes an automated approach to port selection based on a preoperative image of the patient, thus avoiding the need to estimate internal anatomy. Using this image as input, port sites are chosen from a grid of surgeon-approved options by defining a performance measure for each possible port triad. This measure seeks to minimize the weighted squared deviation of the instrument and endoscope angles from their optimal orientations at each internal surgical site. This performance measure proves insensitive to perturbations in both its weighting factors and moderate intraoperative displacements of the patient’s internal anatomy. A validation study of this port site selection was performed. cardiac algorithm also Six surgeons dissected model vessels using the port triad selected by this algorithm with performance compared to dissection using a surgeon-selected port triad and a port triad template described by Tabaie et al., 1999. With the algorithm-selected ports, dissection speed increased by up to 43% (p = 0.046) with less overall vessel trauma. Thus, this algorithmic approach to port site selection has important clinical implications for robot-assisted CABG which warrant further investigation.

This paper presents a novel actuation technology for robotically assisted MRI-guided interventional procedures. Compact and wireless, the actuators are both powered and controlled by the MRI scanner. The design concept and performance limits are described and derived analytically. Simulation and experiments in a clinical MR scanner are used to validate the analysis and to demonstrate the capability of the approach for needle biopsies. The concepts of actuator locking mechanisms and multi-axis control are also introduced.

Three-dimensional ultrasound has been an effective imaging modality for diagnostics and is now an emerging modality for image-guided minimally-invasive interventions since it enables visualization of both instruments and tissue. Challenges to ultrasound-guided interventions arise, however, due to the low signal-to-noise ratio and the imaging artifacts created by the interventional instruments. Metallic instruments, in particular, are strong scatters and so produce a variety of artifacts. For many interventions, the manual or robotic instrument is comprised of a long curved tubular structure with specialized tooling at its tip. Toward the goal of developing a surgical navigation system, this paper proposes an image processing algorithm for enhancing the tubular structure of imaged instruments while also reducing imaging artifacts. Experiments are presented to evaluate the effectiveness of the approach in the context of robotic instruments whose shape comprises a smooth curve along their length.

A family of passive echogenic markers is presented by which the position and orientation of a surgical instrument can be determined in a 3-D ultrasound volume, using simple image processing. Markers are attached near the distal end of the instrument so that they appear in the ultrasound volume along with the instrument tip. They are detected and measured within the ultrasound image, thus requiring no external tracking device. This approach facilitates imaging instruments and tissue simultaneously in ultrasound-guided interventions. Marker-based estimates of instrument pose can be used in augmented reality displays or for image-based servoing. Design principles for marker shapes are presented that ensure imaging system and measurement uniqueness constraints are met. An error analysis is included that can be used to guide marker design and which also establishes a lower bound on measurement uncertainty. Finally, examples of marker measurement and tracking algorithms are presented along with experimental validation of the concepts.

Achieving superior outcomes through the use of robots in medical applications requires an integrated approach to the design of the robot, tooling and the procedure itself. In this paper, this approach is applied to develop a robotic technique for closing abnormal communication between the atria of the heart. The goal is to achieve the efficacy of surgical closure as performed on a stopped, open heart with the reduced risk and trauma of a beating-heart catheter-based procedure. In the proposed approach, a concentric tube robot is used to percutaneously access the right atrium and deploy a tissue approximation device. The device is constructed using a metal microelectromechanical system (MEMS) fabrication process and is designed to both fit the manipulation capabilities of the robot as well as to reproduce the beneficial features of surgical closure by suture. The effectiveness of the approach is demonstrated through ex vivo and in vivo experiments.

Many applications can be imagined for a system that processes sensory information collected during telemanipulation tasks in order to automatically identify properties of the remote environment. These applications include generating model-based simulations for training operators in critical procedures and improving real-time performance in unstructured environments or when time delays are large. This paper explores the research issues involved in developing such an identification system, focusing on properties that can be identified from remote manipulator motion and force data. As a case study, a simple block-stacking task, performed with a teleoperated two-fingered planar hand, is considered. An algorithm is presented that automatically segments the data collected during the task, given only a general description of the temporal sequence of task events. Using the segmented data, the algorithm then successfully estimates the weight, width, height, and coefficient of friction of the two blocks handled during the task. This data is used to calibrate a virtual model incorporating visual and haptic feedback. This case study highlights the broader research issues that must be addressed in automatic property identification.

This paper introduces the first stiffness controller for continuum robots. The control law is based on an accurate approximation of a continuum robot’s coupled kinematic and static force model. To implement a desired tip stiffness, the controller drives the actuators to positions corresponding to a deflected robot configuration that produces the required tip force for the measured tip position. This approach provides several important advantages. First, it enables the use of robot deflection sensing as a means to both sense and control tip forces. Second, it enables stiffness control to be implemented by modification of existing continuum robot position controllers. The proposed controller is demonstrated experimentally in the context of a concentric tube robot. Results show that the stiffness controller achieves the desired stiffness in steady state, provides good dynamic performance, and exhibits stability during contact transitions.

Kinematic models of concentric tube robots have matured from considering only tube bending to considering tube twisting as well as external loading. While these models have been demonstrated to approximate actual behavior, modeling error can be significant for medical applications that often call for positioning accuracy of 1–2mm. As an alternative to moving to more complex models, this paper proposes using sensing to adaptively update model parameters during robot operation. Advantages of this method are that the model is constantly tuning itself to provide high accuracy in the region of the workspace where it is currently operating. It also adapts automatically to changes in robot shape and compliance associated with the insertion and removal of tools through its lumen. As an initial exploration of this approach, a recursive on-line estimator is proposed and evaluated experimentally.